Welcome!

So you’re thinking about leaving the comforting womb of university, and transitioning to a data science career in industry? You may be feeling….

• Overwhelmed by the possibilities
• Confused about the terminology
• Insecure about your capacities
• And so many more emotions

Why

Objectives

I’ll do my best to address these objectives in the most data-driven approach I can!

• Dispel some myths about the data science industry
• Tips on how to transition
• Let’s talk about your CV, the job search and the interview
• New directions in the data science industry
• Bestow some glimmer of hope ⭐

A bit about me

I’m the CEO of a health data science startup in Montreal www.precision-analytics.ca

My background

• BA Psychology, Concordia
• MSc Psychology, Université de Montréal
• PhD Epidemiology and Biostatistics, McGill

Our company

• Founded in 2017
• Began as a statistical consultancy, evolved to a blend of Software as a Service
• Work in the biopharmaceutical sector to create tools that combine data storage, analysis and communication

Our story

What is a data scientist

A combination of programming, statistics and domain knowledge.

I like to think of it as story telling using data

Myths about data science

• Myth 1. You need a PhD to become a data scientist

• Myth 2. All data scientists use Python

• Myth 3. Data science == AI & machine learning

• Myth 4. Data science is all about technical skills

Demographics of respondents

Types of data scientist positions

Myth 1. You need a PhD to become a data scientist

Experience of respondents

Myth 2. Primary tools used at work or school

Myth 4. Data science challenges are mostly analytical

The survey did not explicitly ask the way respondents broke up their time. So I did the next best thing… ask twitter

On transitioning

Understand your new “audience”

💰 The currency in academia versus the currency in the private sector 💰

Gaining more experience: Courses

The American Statistical Association held a two-day Data Science summit. 72 educators, researchers and practitioners in statistics, mathematics, computer science, and data science from academia, industry, government and nonprofit gathered to put forth recommendations for future data science programs.

Data science courses should expose students to:

I’d add to this list: study design. Knowing how data was generated/collected will always inform the types of questions you can ask, and appropriate method of analysis.

Build a portfolio

Showcase your work

• Github
• a simple personal website
• Kaggle competitions

Internship

• Get relevant experience, learn about new industry, create a network
• Check out your university coop programs
• Internship programs in industry IVADO labs, Plotly
• Think Tanks Rand opportunities and here

Don’t forget to reach out to your network and tell people you’re looking to get into a new industry

Preparing your cover letter and CV

• Use the cover letter to explain why you’re applying for the job even if your profile doesn’t perfectly match the job description
• Instead of focusing on all the highlights of your academic career, explain WHY you want to work for THAT company

• In general, keep it short and sweet (max 2 pages)
• Academic CV != Professional CV
• Only provide extensive details about academic experience if it is relevant
• Tailor your CV and cover letter to each application - use key words from posting

More on CV’s

A few tips (based on my personal pet peeves)

• Use consistent formatting
• If using a job aggregator site, upload your actual CV (the printout we see is very ugly)
• Don’t include abbreviations or technical jargon
• Don’t indicate that you have high proficiency in 22 programming languages
• Don’t list 30 MOOC’s I know you didn’t finish

Searching for a position

• Focus on the skills listed in the job positing, not the title but be weary of descriptions

• Widen your search beyond “data scientist” - many analogous positions exist with different titles: analyst, bio/statistician, health economist, R/SAS/Python programmer, technical research assistant, quantitative scientist, consultant ect.

• Don’t expect that you will possess 100% of the stated skills; it’s often a wishlist
• Try to talk to people (ideally former employees) to get insider perspective

• Be aware that many companies are jumping on the “data” bandwagon without having any infrastructure to support a junior hire

During the interview

• Expect technical questions and/or coding challenge
• Be honest about your skills
• Be mindful about expressing your desired to be paid to learn
• Highlight the value you bring to the employer, rather than the value you accrued in university
• Be ready to show that you are able to explain technical concepts to non-technical audiences

Jacqueline Nolis’s book Build a Career in Data Science

“I want to hear about a project they’ve worked on recently. I ask them about how the project started, how they determined it was worth time and effort, their process, and their results. I also ask them about what they learned from the project. I gain a lot from answers to this question: if they can tell a narrative, how the problem related to the bigger picture, and how they tackled the hard work of doing something”.

Asking for feedback after your interview can help highlight the interviewer’s perceptions of your strengths and weaknesses

Employer fears

In general, employers are concerned that new hires without job experience will struggle to see the bigger picture and keep up with the pace.

Try to address these fears explicitly in your cover letter, CV and/or during the interview

Conclusion

Pivoting to data science from humanities, social sciences or any other disciplines doesn’t mean you need to leave all of your training behind.

✔️ You have knowledge and/or experience in at least one of these circles

☑️ Look for people in your substantive area who are doing some more quantitative/tech driven work

☑️ Be confident that you know how to learn

☑️ Be ready to rebrand yourself

✔️ Be ready to be creative in your search and to be a hustler

☑️ Your path is the right path

New directions (my hot takes)

• Large push towards “automating” analytics is set to change the role of data scientists (e.g. Data Robot, Alteryx, Dataiku, Google Cloud AI); storytelling and substantive expertise are the last aspects of data and communication that cannot be productized

• Many organizations want to innovate but are not sure how; many ML investments have failed to deliver value

• In the healthcare sector, billions invested in AI for drug discovery but not a single drug has gone to market

A big thanks!

I can be found on twitter @ea_braithwaite

Come check out our website www.precision-analytics.ca

This presentation & R code can be found online https://github.com/precision-analytics/CAnD3.

Simply download the entire repo via “clone/download” button; the presentation is the html file

Happy to answer any questions